March 2024 arXiv papers — page 101

Xuehao Wang, Feiyang Ye, Yu Zhang

The Segment Anything Model (SAM), with its remarkable zero-shot capability, has been proven to be a powerful foundation model for image segmentation tasks, which is an important task in computer vision. However, the transfer of its rich semantic information to multiple different downstream tasks remains unexplored. In this paper, we propose the Task-Aware Lo

Henrique Gomes

The hole argument of general relativity threatens a radical and pernicious form of indeterminism. One natural response to the argument is that points belonging to different but isometric models should always be identified, or 'dragged-along', by the diffeomorphism that relates them. In this paper, I first criticise this response and its construal of isometry

Zong-Xing Xiong, Mao-Sheng Li, Bing Yu, Zhu-Jun Zheng

Recently, there is growing interest in the study of genuine nonlocality, which serves to explore the local accessability of global information encoded in orthogonal multipartite quantum states under scenarios where not all subsystems are joined together. For such form of nonlocality, a probably most fundamental question is upon what states it is prone to be

Ghazaleh Shirvani, Saeid Ghasemshirazi, Mohammad Ali Alipour

The rapid proliferation of the Internet of Things (IoT) has ushered in transformative connectivity between physical devices and the digital realm. Nonetheless, the escalating threat of Distributed Denial of Service (DDoS) attacks jeopardizes the integrity and reliability of IoT networks. Conventional DDoS mitigation approaches are ill-equipped to handle the

Sai Prasanna, Karim Farid, Raghu Rajan, André Biedenkapp

Zero-shot generalization (ZSG) to unseen dynamics is a major challenge for creating generally capable embodied agents. To address the broader challenge, we start with the simpler setting of contextual reinforcement learning (cRL), assuming observability of the context values that parameterize the variation in the system's dynamics, such as the mass or dimens

Federico Girlanda, Lasse Shala, Shivesh Kumar, Frank Kirchner

Optimal behaviours of a system to perform a specific task can be achieved by leveraging the coupling between trajectory optimization, stabilization, and design optimization. This approach is particularly advantageous for underactuated systems, which are systems that have fewer actuators than degrees of freedom and thus require for more elaborate control syst

Pulkit Mundra, Veni Goyal, Kusum Deep

This paper deals with the problem of circle packing, in which the largest radii circle is to be fit in a confined space filled with arbitrary circles of different radii and centers. A circle packing problem is one of a variety of cutting and packing problems. We suggest four different nature-inspired Meta-heuristic algorithms to solve this problem. Algorithm

Matthew Sutcliffe, Aleks Kissinger

A quantum circuit may be strongly classically simulated with the aid of ZX-calculus by decomposing its $t$ T-gates into a sum of $2^{\alpha t}$ classically computable stabiliser terms. In this paper, we introduce a general procedure to find an optimal pattern of vertex cuts in a ZX-diagram to maximise its T-count reduction at the cost of the fewest cuts. Rat

Niyati Bafna, Philipp Koehn, David Yarowsky

While Transformer-based neural machine translation (NMT) is very effective in high-resource settings, many languages lack the necessary large parallel corpora to benefit from it. In the context of low-resource (LR) MT between two closely-related languages, a natural intuition is to seek benefits from structural "shortcuts", such as copying subwords from the

Baiyuan Chen

This paper presents an innovative enhancement to the Sphere as Prior Generative Adversarial Network (SP-GAN) model, a state-of-the-art GAN designed for point cloud generation. A novel method is introduced for point cloud generation that elevates the structural integrity and overall quality of the generated point clouds by incorporating topological priors int

Zhijian Ou

Energy-Based Models (EBMs) are an important class of probabilistic models, also known as random fields and undirected graphical models. EBMs are un-normalized and thus radically different from other popular self-normalized probabilistic models such as hidden Markov models (HMMs), autoregressive models, generative adversarial nets (GANs) and variational auto-

Julian Maisch, Jonas Grammel, Nam Tran, Michael Jetter

Single-photon emitters integrated in optical micro-cavities are key elements in quantum communication applications. However, optimizing their emission properties and achieving efficient cavity coupling remain significant challenges. In this study, we investigate semiconductor quantum dots (QDs) emitting in the telecom O-band and integrate them in an open fib

Pablo Carrillo, Damien Galant, Louis Jeanjean, Christophe Troestler

We consider the existence of solutions for nonlinear Schr\"odinger equations on noncompact metric graphs with localized nonlinearities. In an $L^2$-supercritical regime, we establish the existence of infinitely many solutions for any prescribed mass.

Tamal K. Dey, Florian Russold, Shreyas N. Samaga

We extend the persistence algorithm, viewed as an algorithm computing the homology of a complex of free persistence or graded modules, to complexes of modules that are not free. We replace persistence modules by their presentations and develop an efficient algorithm to compute the homology of a complex of presentations. To deal with inputs that are not given

Boštjan Brešar, Jaka Hedžet, Rebekah Herrman

The $r$-neighbor bootstrap percolation is a graph infection process based on the update rule by which a vertex with $r$ infected neighbors becomes infected. We say that an initial set of infected vertices propagates if all vertices of a graph $G$ are eventually infected, and the minimum cardinality of such a set in $G$ is called the $r$-bootstrap percolation

Santosh V. Lohakare, B. Mishra

In this work, we explore the cosmological stability of $f(Q, B)$ gravity using a dynamical system approach, where $Q$ denotes the nonmetricity scalar and $B$ represents the boundary term. We determine the model parameters of $f(Q, B)$ through Bayesian statistical analysis, employing Markov Chain Monte Carlo techniques. This analysis incorporates numerical so

Trevor Exley, Rashmi Wijesundara, Nathan Tan, Akshay Sunkara

In this study, we introduce a novel Mylar-based pouch motor design that leverages the reversible actuation capabilities of Peltier junctions to enable agonist-antagonist muscle mimicry in soft robotics. Addressing the limitations of traditional silicone-based materials, such as leakage and phase-change fluid degradation, our pouch motors filled with Novec 70

Lefteris Mamatas, Sotiris Skaperas, Ilias Sakellariou

We demonstrate ClusterSlice, an open-source solution for automated Kubernetes-center deployments for the edge continuum. ClusterSlice is an infrastructure-as-a-service, platform-as-a-service, and application-as-a-service solution, supporting: (i) declarative deployment slice definitions; (ii) infrastructure-on-demand capabilities over multiple heterogeneous

Hongxiang Zhao, Xili Dai, Jianan Wang, Shengbang Tong

Large image diffusion models have demonstrated zero-shot capability in novel view synthesis (NVS). However, existing diffusion-based NVS methods struggle to generate novel views that are accurately consistent with the corresponding ground truth poses and appearances, even on the training set. This consequently limits the performance of downstream tasks, such

Jiming Zheng, Zhiyue Lu

Fluctuation-dissipation relations elucidate the response of near-equilibrium systems to environmental changes, with recent advances extending response theory to non-equilibrium steady states. However, a general response theory for systems evolving far from steady states has remained elusive. This letter presents a complete trajectory information geometric fr

Trevor Exley, Rashmi Wijesundara, Shuopu Wang, Arian Moridani

In this work, we introduce an advanced thermo-active variable impedance module which builds upon our previous innovation in thermal-based impedance adjustment for actuation systems. Our initial design harnessed the temperature-responsive, viscoelastic properties of Polycaprolactone (PCL) to modulate stiffness and damping, facilitated by integrated flexible P

Nagula Venkata Anirudh, Sachidananda Behera, Kirti Chandra Sahu

We employ three-dimensional numerical simulations to explore the impact dynamics of non-spherical drops in a deep liquid pool by varying the aspect ratios $(A_r)$ and Weber numbers $(\We)$. We observe that when a non-spherical drop is gently placed on a liquid pool, it exhibits a partial coalescence phenomenon and the emergence of a daughter droplet for $A_r

Jixuan He, Yutong Guo, Jiacheng Zhao

This paper delves into the paramount significance of information dissemination within the dynamic realm of social networks. It underscores the pivotal role of information communication models in unraveling the intricacies of data propagation in the digital age. By shedding light on the profound influence of these models, it not only lays the groundwork for e

Haozhe Chen, Carl Vondrick, Chengzhi Mao

How do large language models (LLMs) obtain their answers? The ability to explain and control an LLM's reasoning process is key for reliability, transparency, and future model developments. We propose SelfIE (Self-Interpretation of Embeddings), a framework that enables LLMs to interpret their own embeddings in natural language by leveraging their ability to r

Donghoon Baek, Youngwoo Sim, Amartya Purushottam, Saurabh Gupta

Model-based controllers using a linearized model around the system's equilibrium point is a common approach in the control of a wheeled humanoid due to their less computational load and ease of stability analysis. However, controlling a wheeled humanoid robot while it lifts an unknown object presents significant challenges, primarily due to the lack of knowl

Christoph Hurm, Patrik Knopf, Andrea Poiatti

The main goal of this paper is to establish the nonlocal-to-local convergence of strong solutions to a Navier--Stokes--Cahn--Hilliard model with singular potential describing immiscible, viscous two-phase flows with matched densities, which is referred to as the Model H. This means that we show that the strong solutions to the nonlocal Model H converge to th

Ziping Xu, Kelly W. Zhang, Susan A. Murphy

Online Reinforcement Learning (RL) is typically framed as the process of minimizing cumulative regret (CR) through interactions with an unknown environment. However, real-world RL applications usually involve a sequence of tasks, and the data collected in the first task is used to warm-start the second task. The performance of the warm-start policy is measur

Geonhee Han, Kaoru Irie

The disaggregated time-series for the Consumer Price Index (CPI) often exhibits exact zero price changes, stemming from structural features of the data collection process. However, the currently prominent stochastic volatility model of trend-inflation is designed for aggregate measures of price inflation, where zeros rarely occur. We formulate a zero-inflate

Sudipto Ghosh, Devanshu Verma, Balaji Ganesan, Purnima Bindal

Legal research is a crucial task in the practice of law. It requires intense human effort and intellectual prudence to research a legal case and prepare arguments. Recent boom in generative AI has not translated to proportionate rise in impactful legal applications, because of low trustworthiness and and the scarcity of specialized datasets for training Larg

Hanlei Zhang, Xin Wang, Hua Xu, Qianrui Zhou

Multimodal intent recognition poses significant challenges, requiring the incorporation of non-verbal modalities from real-world contexts to enhance the comprehension of human intentions. Existing benchmark datasets are limited in scale and suffer from difficulties in handling out-of-scope samples that arise in multi-turn conversational interactions. We intr

Junggu Choi, Tak Hur, Daniel K. Park, Na-Young Shin

Following the recent development of quantum machine learning techniques, the literature has reported several quantum machine learning algorithms for disease detection. This study explores the application of a hybrid quantum-classical algorithm for classifying region-of-interest time-series data obtained from resting-state functional magnetic resonance imagin

Federico Nocentini, Thomas Besnier, Claudio Ferrari, Sylvain Arguillere

Speech-driven 3D talking heads generation has emerged as a significant area of interest among researchers, presenting numerous challenges. Existing methods are constrained by animating faces with fixed topologies, wherein point-wise correspondence is established, and the number and order of points remains consistent across all identities the model can animat

G. Garcia-Arellano, G. I. López-Morales, N. B. Manson, J. Flick

The silicon vacancy (SiV) center in diamond is drawing much attention due to its optical and spin properties, attractive for quantum information processing and sensing. Comparatively little is known, however, about the dynamics governing SiV charge state interconversion mainly due to challenges associated with generating, stabilizing, and characterizing all

Claude Pruneau, Sumit Basu, Victor Gonzalez, Brian Hanley

Mixed species charge and baryon balance functions are computed based on proton--proton (pp) collisions simulated with the PYTHIA8 model. Simulations are performed with selected values of the collision energy $\sqrt{s}$ and the Monash tune and the Ropes and Shoving modes of PYTHIA8 to explore whether such measurements provide useful new information and constr

Anthony Liang, Jesse Thomason, Erdem Bıyık

Training robots to perform complex control tasks from high-dimensional pixel input using reinforcement learning (RL) is sample-inefficient, because image observations are comprised primarily of task-irrelevant information. By contrast, humans are able to visually attend to task-relevant objects and areas. Based on this insight, we introduce Visual Saliency-G

Georgios Sidiropoulos, Evangelos Kanoulas

Dense retrieval has become the new paradigm in passage retrieval. Despite its effectiveness on typo-free queries, it is not robust when dealing with queries that contain typos. Current works on improving the typo-robustness of dense retrievers combine (i) data augmentation to obtain the typoed queries during training time with (ii) additional robustifying su

Meghdad Saeedian, Chengyi Tu, Fabio Menegazzo, Paolo D'Odorico

Games with environmental feedback have become a crucial area of study across various scientific domains, modelling the dynamic interplay between human decisions and environmental changes, and highlighting the consequences of our choices on natural resources and biodiversity. In this work, we propose a co-evolutionary model for human-environment systems that

Chengjie Ma

A novel federated learning training framework for heterogeneous environments is presented, taking into account the diverse network speeds of clients in realistic settings. This framework integrates asynchronous learning algorithms and pruning techniques, effectively addressing the inefficiencies of traditional federated learning algorithms in scenarios invol

Savitha Murthy, Dinkar Sitaram

This paper addresses the problem of improving speech recognition accuracy with lattice rescoring in low-resource languages where the baseline language model is insufficient for generating inclusive lattices. We minimally augment the baseline language model with word unigram counts that are present in a larger text corpus of the target language but absent in

Farhad Pakdaman, Moncef Gabbouj

The emerging Learned Compression (LC) replaces the traditional codec modules with Deep Neural Networks (DNN), which are trained end-to-end for rate-distortion performance. This approach is considered as the future of image/video compression, and major efforts have been dedicated to improving its compression efficiency. However, most proposed works target com

Chengbin Du, Yanxi Li, Chang Xu

Visual State Space Model (VMamba) has recently emerged as a promising architecture, exhibiting remarkable performance in various computer vision tasks. However, its robustness has not yet been thoroughly studied. In this paper, we delve into the robustness of this architecture through comprehensive investigations from multiple perspectives. Firstly, we inves

Amin Yazdanshenas, Reza Faieghi

Despite extensive research on sliding mode control (SMC) design for quadrotors, the existing approaches suffer from certain limitations. Euler angle-based SMC formulations suffer from poor performance in high-pitch or -roll maneuvers. Quaternion-based SMC approaches have unwinding issues and complex architecture. Coordinate-free methods are slow and only alm

Fernando Henríquez, José Pinto

We consider the elastic scattering problem by multiple disjoint arcs or \emph{cracks} in two spatial dimensions. A key aspect of our approach lies in the parametric description of each arc's shape, which is controlled by a potentially high-dimensional, possibly countably infinite, set of parameters. We are interested in the efficient approximation of the par

Shiva Kumar Tekumatla, Varun Gampa, Siavash Farzan

This paper presents an off-policy Gaussian Predictive Control (GPC) framework aimed at solving optimal control problems with a smaller computational footprint, thereby facilitating real-time applicability while ensuring critical safety considerations. The proposed controller imitates classical control methodologies by modeling the optimization process throug

Mingzhou Jiang, Jiaying Zhou, Junde Wu, Tianyang Wang

Collective intelligence from multiple medical experts consistently surpasses individual expertise in clinical diagnosis, particularly for ambiguous medical image segmentation tasks involving unclear tissue boundaries or pathological variations. The Segment Anything Model (SAM), a powerful vision foundation model originally designed for natural image segmenta

Huifan Gao, Yifeng Zeng, Yinghui Pan

Optimizing students' learning strategies is a crucial component in intelligent tutoring systems. Previous research has demonstrated the effectiveness of devising personalized learning strategies for students by modelling their learning processes through partially observable Markov decision process (POMDP). However, the research holds the assumption that the

Aidan Scannell, Riccardo Mereu, Paul Chang, Ella Tamir

Sequential learning paradigms pose challenges for gradient-based deep learning due to difficulties incorporating new data and retaining prior knowledge. While Gaussian processes elegantly tackle these problems, they struggle with scalability and handling rich inputs, such as images. To address these issues, we introduce a technique that converts neural netwo

Anatolii V. Mokshin, Roman V. Vlasov

In equilibrium and supercooled liquids, polymorphism is manifested by thermodynamic regions defined in the phase diagram, which are predominantly of different short- and medium-range order (local structure). It is found that on the phase diagram of the water model, the thermodynamic region corresponding to the equilibrium liquid phase is divided by a line of

Yang Huang, Miaomiao Dong, Yijie Mao, Wenqiang Liu

Utilizing unmanned aerial vehicles (UAVs) with edge server to assist terrestrial mobile edge computing (MEC) has attracted tremendous attention. Nevertheless, state-of-the-art schemes based on deterministic optimizations or single-objective reinforcement learning (RL) cannot reduce the backlog of task bits and simultaneously improve energy efficiency in high

David J. Aldous, Guillaume Blanc, Nicolas Curien

What distributions arise as the distribution of the distance between two typical points in some measured metric space? This seems to be a surprisingly subtle problem. We conjecture that every distribution with a density function whose support contains $0$ does arise in this way, and give some partial results in that direction.

Zhiheng Li, Muheng Li, Jixuan Fan, Lei Chen

Scale arbitrary super-resolution based on implicit image function gains increasing popularity since it can better represent the visual world in a continuous manner. However, existing scale arbitrary works are trained and evaluated on simulated datasets, where low-resolution images are generated from their ground truths by the simplest bicubic downsampling. T

Akshay Jaitly, Siavash Farzan

Optimization methods for long-horizon, dynamically feasible motion planning in robotics tackle challenging non-convex and discontinuous optimization problems. Traditional methods often falter due to the nonlinear characteristics of these problems. We introduce a technique that utilizes learned representations of the system, known as Polytopic Action Sets, to

David Rundel, Julius Kobialka, Constantin von Crailsheim, Matthias Feurer

The recently developed Prior-Data Fitted Networks (PFNs) have shown very promising results for applications in low-data regimes. The TabPFN model, a special case of PFNs for tabular data, is able to achieve state-of-the-art performance on a variety of classification tasks while producing posterior predictive distributions in mere seconds by in-context learni

Su-Yan Pei, Wei Li, Tianhong Wang, Guo-Li Wang

In the framework of instantaneous Bethe-Salpeter equation, according to the $J ^ {PC}$ of quarkonia, we find that their wave functions all contain multiple partial waves, rather than pure waves. In the radiative electromagnetic transitions $\chi_{_{cJ}}$$\rightarrow$$\gamma\psi$ and $\chi_{_{bJ}}$$\rightarrow$$\gamma\Upsilon$ ($J=0,1,2$), the main wave of qu

Kangchun Zhao, Yijie Mao, Yuanming Shi

In this work, we unveil the advantages of synergizing cooperative rate splitting (CRS) with user relaying and simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR RIS). Specifically, we propose a novel STAR RIS-assisted CRS transmission framework, featuring six unique transmission modes that leverage various combination of the

Peng Zhang, Ao Duan, Xianglu Zou, Yuhong Liu

Privacy-Preserving Neural Networks (PPNN) are advanced to perform inference without breaching user privacy, which can serve as an essential tool for medical diagnosis to simultaneously achieve big data utility and privacy protection. As one of the key techniques to enable PPNN, Fully Homomorphic Encryption (FHE) is facing a great challenge that homomorphic o

Daisuke Ishii

The compositional approach is important for reasoning about large and complex systems. In this work, we address synchronous systems with hierarchical structures, which are often used to model cyber-physical systems. We revisit the theory of reactive modules and reformulate it based on hypergraphs to clarify the parallel composition and the hierarchical descr

Arafat Islam, Md. Imtiaz Habib

Graph labeling is a technique that assigns unique labels or weights to the vertices or edges of a graph, often used to analyze and solve various graph-related problems. There are few methods with certain limitations conducted by researchers previously on this topic. This research paper focuses on antimagic labeling of different types of graphs and trees. It

Luigi Delle Site, Carsten Hartmann

In this paper we study computationally feasible bounds for relative free energies between two many-particle systems. Specifically, we consider systems out of equilibrium that do not necessarily satisfy a fluctuation-dissipation relation, but that nevertheless admit a nonequilibrium steady state that is reached asymptotically in the long-time limit. The bound

Elena Gaburro

In this work, we present a novel family of high order accurate numerical schemes for the solution of hyperbolic partial differential equations (PDEs) which combines several geometrical and physical structure preserving properties. First, we settle our methods in the Lagrangian framework, where each element of the mesh evolves following as close as possible t

Moseli Mots'oehli, Anton Nikolaev, Wawan B. IGede, John Lynham

Fish stock assessment often involves manual fish counting by taxonomy specialists, which is both time-consuming and costly. We propose FishNet, an automated computer vision system for both taxonomic classification and fish size estimation from images captured with a low-cost digital camera. The system first performs object detection and segmentation using a

Oleksiy Karlovych, Alina Shalukhina

Let $(X,d,\mu)$ be a space of homogeneous type and $p(\cdot):X\to[1,\infty]$ be a variable exponent. We show that if the measure $\mu$ is Borel-semiregular and reverse doubling, then the condition ${\rm ess\,inf}_{x\in X}p(x)>1$ is necessary for the boundedness of the Hardy-Littlewood maximal operator $M$ on the variable Lebesgue space $L^{p(\cdot)}(X,d,\mu)

Yansong Xu, Dongxu Lyu, Zhenyu Li, Zilong Wang

Multi-scale deformable attention (MSDeformAttn) has emerged as a key mechanism in various vision tasks, demonstrating explicit superiority attributed to multi-scale grid-sampling. However, this newly introduced operator incurs irregular data access and enormous memory requirement, leading to severe PE underutilization. Meanwhile, existing approaches for atte

Anjali Karangiya, Anirudh Sharma, Divax Shah, Kartavya Badgujar

The proliferation of digital images and the advancements in deep learning have paved the way for innovative solutions in various domains, especially in the field of image classification. Our project presents an in-depth study and implementation of an image classification system specifically tailored to identify and classify images of Indian cities. Drawing f

Yeongtak Oh, Jonghyun Lee, Jooyoung Choi, Dahuin Jung

Test-time adaptation (TTA) addresses the unforeseen distribution shifts occurring during test time. In TTA, performance, memory consumption, and time consumption are crucial considerations. A recent diffusion-based TTA approach for restoring corrupted images involves image-level updates. However, using pixel space diffusion significantly increases resource r

Zhen Wang, Wenwen Min

Nonnegative Matrix Factorization (NMF) is a widely applied technique in the fields of machine learning and data mining. Graph Regularized Non-negative Matrix Factorization (GNMF) is an extension of NMF that incorporates graph regularization constraints. GNMF has demonstrated exceptional performance in clustering and dimensionality reduction, effectively disc

José F. Alves, Wael Bahsoun

We study semiflows generated via impulsive perturbations of Lorenz flows. We prove that such semiflows admit a finite number of physical measures. Moreover, if the impulsive perturbation is small enough, we show that the physical measures of the semiflows are close, in the weak* topology, to the unique physical measure of the Lorenz flow. A similar conclusio

Ankit Dulat, Sagar Dam, Sk Rakeeb, Amit D. Lad

The complex interaction dynamics of intense femtosecond (fs) pulses and their picosecond (ps)-long leading edge with nanostructured solids occur at both the nanometer and the femtosecond scales, making them extremely difficult to measure directly. Here, we present pump-probe-based measurements that capture the ultrafast evolution of relativistically intense

Emanuele Bacchiocchi, Andrea Bastianin, Graziano Moramarco

We estimate the short-run effects of weather-related disasters on local economic activity and cross-border spillovers that operate through economic linkages between U.S. states. To this end, we use emergency declarations triggered by natural disasters and estimate their effects using a monthly Global Vector Autoregressive (GVAR) model for U.S. states. Impuls

Seunghyeon Seo, Yeonjin Chang, Jayeon Yoo, Seungwoo Lee

Recent advancements in the Neural Radiance Field (NeRF) have enhanced its capabilities for novel view synthesis, yet its reliance on dense multi-view training images poses a practical challenge, often leading to artifacts and a lack of fine object details. Addressing this, we propose ARC-NeRF, an effective regularization-based approach with a novel Area Ray

Sayan Chowdhury, Satya Kiran Raju Alluri, Jayaprakash J, Fang Yenn Teo

Mega-engineered hydraulic structures like dams and barrages are critically sensitive to strong ground motion if constructed within the vicinity of triggered fault lines. Collapse post excessive deformation leads to severe environmental impact. In this study, fracture corresponding to the response of a concrete tidal barrage to strong ground motion is analyze

Martin Spitznagel, Janis Keuper

Data-driven modeling of complex physical systems is receiving a growing amount of attention in the simulation and machine learning communities. Since most physical simulations are based on compute-intensive, iterative implementations of differential equation systems, a (partial) replacement with learned, 1-step inference models has the potential for signific

Riccardo Crupi, Daniele Regoli, Alessandro Damiano Sabatino, Immacolata Marano

Explaining outliers occurrence and mechanism of their occurrence can be extremely important in a variety of domains. Malfunctions, frauds, threats, in addition to being correctly identified, oftentimes need a valid explanation in order to effectively perform actionable counteracts. The ever more widespread use of sophisticated Machine Learning approach to id

Silvia Manconi, Jooyun Woo, Ruo-Yu Shang, Roman Krivonos

Geminga is the first pulsar around which a remarkable TeV gamma-ray halo extending over a few degrees was discovered by MILAGRO, HAWC and later by H.E.S.S., and by Fermi-LAT in the GeV band. More middle-aged pulsars have exhibited gamma-ray halos, and they are now recognized as an emerging class of Galactic gamma-ray sources. The emission appears in the late

E. Yu. Panov

We study self-similar solutions of a multi-phase Stefan problem for a heat equation on the half-line $x>0$ with a constant initial data and with Dirichlet or Neumann boundary conditions. In the case of Dirichlet boundary condition we prove that a nonlinear algebraic system for determination of the free boundaries is gradient one and the corresponding potenti

Divyansh Dewan, Archana Soam, Guo-Yin Zhang, Akhil Lasrado

Context: Molecular clouds are the prime locations of star formation. These clouds contain filamentary structures and cores which are crucial in the formation of young stars. Aims: In this work, we aim to quantify the physical properties of structural characteristics within the molecular cloud L1251 to better understand the initial conditions for star formati

Sheikh Shafayat, H M Quamran Hasan, Minhajur Rahman Chowdhury Mahim, Rifki Afina Putri

In this study, we introduce BEnQA, a dataset comprising parallel Bengali and English exam questions for middle and high school levels in Bangladesh. Our dataset consists of approximately 5K questions covering several subjects in science with different types of questions, including factual, application, and reasoning-based questions. We benchmark several Larg

Vygintas Gontis, Aleksejus Kononovicius, Julius Ruseckas

Physics research complements traditional approaches, such as mathematical (stochastic) finance and econometrics in quantitative economics and finance. In the early years of this millennium, we embarked on an interdisciplinary research endeavor in Lithuania, applying concepts from statistical physics to understand complex financial and social systems. Here, w

V. M. Kovalev, A. V. Parafilo, O. V. Kibis, I. G. Savenko

We develop a theory of Coulomb interaction-related contribution to the photogalvanic current of the carriers of charge in two-dimensional non-centrosymmetric Dirac materials possessing a nontrivial structure of valleys and exposed to an external electromagnetic field. The valley photogalvanic effect occurs here due to the trigonal warping of electrons and ho

Guanzhou Ke, Bo Wang, Xiaoli Wang, Shengfeng He

Multi-view representation learning aims to derive robust representations that are both view-consistent and view-specific from diverse data sources. This paper presents an in-depth analysis of existing approaches in this domain, highlighting a commonly overlooked aspect: the redundancy between view-consistent and view-specific representations. To this end, we

Daniela Scherer dos Santos, Kathrin Klamroth, Pedro Martins, Luís Paquete

Given a simple undirected graph $G$, a quasi-clique is a subgraph of $G$ whose density is at least $\gamma$ $(0 < \gamma \leq 1)$. Finding a maximum quasi-clique has been addressed from two different perspectives: $i)$ maximizing vertex cardinality for a given edge density; and $ii)$ maximizing edge density for a given vertex cardinality. However, when no a

Zihan Wang, Jiayu Xiao, Mengxiang Li, Zhongjiang He

In our dynamic world where data arrives in a continuous stream, continual learning enables us to incrementally add new tasks/domains without the need to retrain from scratch. A major challenge in continual learning of language model is catastrophic forgetting, the tendency of models to forget knowledge from previously trained tasks/domains when training on n

Rui Min, Sen Li, Hongyang Chen, Minhao Cheng

The ethical need to protect AI-generated content has been a significant concern in recent years. While existing watermarking strategies have demonstrated success in detecting synthetic content (detection), there has been limited exploration in identifying the users responsible for generating these outputs from a single model (owner identification). In this p

Mostafa Bendahmane, Youssef Ouakrim, Yassine Ouzrour, Mohamed Zagour

This paper presents a nonlinear reaction-diffusion-fluid system that simulates radiofrequency ablation within cardiac tissue. The model conveys the dynamic evolution of temperature and electric potential in both the fluid and solid regions, along with the evolution of velocity within the solid region. By formulating the system that describes the phenomena ac

Frank Smallenburg, Giovanni Del Monte, Marjolein de Jager, Laura Filion

One method for computationally determining phase boundaries is to explicitly simulate a direct coexistence between the two phases of interest. Although this approach works very well for fluid-fluid coexistences, it is often considered to be less useful for fluid-crystal transitions, as additional care must be taken to prevent the simulation boundaries from i

Milad Mousavi, Yannis Dimakopoulos, John Tsamopoulos

We present predictions for the flow of elastoviscoplastic (EVP) fluids in the 4 to 1 planar contraction geometry. The Saramito-Herschel-Bulkley fluid model is solved via the finite-volume method with the OpenFOAM software. Both the constitutive model and the solution method require using transient simulations. In this benchmark geometry, whereas viscoelastic

Steve Hanneke, Shay Moran, Tom Waknine

List learning is a variant of supervised classification where the learner outputs multiple plausible labels for each instance rather than just one. We investigate classical principles related to generalization within the context of list learning. Our primary goal is to determine whether classical principles in the PAC setting retain their applicability in th

Ali Cici, Huseyin Dag

In 2022, the CDF Collaboration reported the $W$-boson mass, $M_W=80.4335\pm0.0094~\mathrm{GeV}$, which deviates from the Standard Model (SM) prediction, $M_W^{\rm SM}=80.357\pm0.006~\mathrm{GeV}$, by about $7\sigma$. By contrast, the CMS Collaboration obtained $M_W=80.3602\pm0.0099~\mathrm{GeV}$, very close to the SM global electroweak fit value of $\sim80.3

Yuanxin Zhao, Mi Zhang, Bingnan Yang, Zhan Zhang

Image-text retrieval (ITR) plays a significant role in making informed decisions for various remote sensing (RS) applications. Nonetheless, creating ITR datasets containing vision and language modalities not only requires significant geo-spatial sampling area but also varing categories and detailed descriptions. To this end, we introduce an image caption dat

Mark Pankov

Let $H$ be an infinite-dimensional complex Hilbert space and let ${\mathcal G}_{\infty}(H)$ be the set of all closed subspaces of $H$ whose dimension and codimension both are infinite. We investigate (not necessarily surjective) transformations of ${\mathcal G}_{\infty}(H)$ sending every pair of subspaces to an equivalent pair of subspaces; two pairs of subs

Soumyajyoti Dey, Sukanta Chakraborty, Utso Guha Roy, Nibaran Das

Automation in medical imaging is quite challenging due to the unavailability of annotated datasets and the scarcity of domain experts. In recent years, deep learning techniques have solved some complex medical imaging tasks like disease classification, important object localization, segmentation, etc. However, most of the task requires a large amount of anno

Shashi Shekhar Kumar, Ritesh Chandra, Sonali Agarwal

In recent years, smart city-based development has gained momentum due to its versatile nature in architecture and planning for the systematic habitation of human beings. According to World Health Organization (WHO) report, air pollution causes serious respiratory diseases. Hence, it becomes necessary to real-time monitoring of air quality to minimize effect

Soumyajyoti Dey, Sukanta Chakraborty, Utso Guha Roy, Nibaran Das

Cytology image segmentation is quite challenging due to its complex cellular structure and multiple overlapping regions. On the other hand, for supervised machine learning techniques, we need a large amount of annotated data, which is costly. In recent years, late fusion techniques have given some promising performances in the field of image classification.

Jiyuan Fu, Zhaoyu Chen, Kaixun Jiang, Haijing Guo

Despite the substantial advancements in Vision-Language Pre-training (VLP) models, their susceptibility to adversarial attacks poses a significant challenge. Existing work rarely studies the transferability of attacks on VLP models, resulting in a substantial performance gap from white-box attacks. We observe that prior work overlooks the interaction mechani

ChangSu Choi, Yongbin Jeong, Seoyoon Park, InHo Won

Large language models (LLMs) use pretraining to predict the subsequent word; however, their expansion requires significant computing resources. Numerous big tech companies and research institutes have developed multilingual LLMs (MLLMs) to meet current demands, overlooking less-resourced languages (LRLs). This study proposed three strategies to enhance the p

Somenath Kuiry, Alaka Das, Mita Nasipuri, Nibaran Das

Deep Learning, particularly Convolutional Neural Networks (CNN), has been successful in computer vision tasks and medical image analysis. However, modern CNNs can be overconfident, making them difficult to deploy in real-world scenarios. Researchers propose regularizing techniques, such as Label Smoothing (LS), which introduces soft labels for training data,

Anay Panja, Somenath Kuiry, Alaka Das, Mita Nasipuri

Since 2019, the global COVID-19 outbreak has emerged as a crucial focus in healthcare research. Although RT-PCR stands as the primary method for COVID-19 detection, its extended detection time poses a significant challenge. Consequently, supplementing RT-PCR with the pathological study of COVID-19 through CT imaging has become imperative. The current segment

Dechao Kong, Xiaoqi Li, Wenkai Li

Non-Fungible Tokens (NFTs) are digital assets recorded on the blockchain, providing cryptographic proof of ownership over digital or physical items. Although Solana has only begun to gain popularity in recent years, its NFT market has seen substantial transaction volumes. In this paper, we conduct the first systematic research on the characteristics of Solan

Nicoletta D'Angelo, Giada Adelfio

This work presents the cubature scheme for the fitting of spatio-temporal Poisson point processes. The methodology is implemented in the R Core Team (2024) package stopp (D'Angelo and Adelfio, 2023), published on the Comprehensive R Archive Network (CRAN) and available from https://CRAN.R-project.org/package=stopp. Since the number of dummy points should be

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

We report a first study of the semileptonic decay $D^0\rightarrow K^-\pi^0\mu^{+}\nu_{\mu}$ by analyzing an $e^+e^-$ annihilation data sample of $7.9~\mathrm{fb}^{-1}$ collected at the center-of-mass energy of 3.773 GeV with the BESIII detector. The absolute branching fraction of $D^0\to K^-\pi^0\mu^{+}\nu_{\mu}$ is measured for the first time to be $(0.729